HW5 - Q7
Matilde Jacobsen
19/10/2020
Set working directory
As a standard I always start out by setting my working directory to the current folder:
#Setting working directory
setwd("/Users/matilde/Desktop/AU/Cultural Data Science/R/CDS2020_1")Get the necessary packages
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
#Q7 Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]
#Gapminder research question Does pollusion go up as people get more money? Does co2 emission per capita rise with income per capita?
#loading an extented version of Gapminder
gapminder2 <- gapminder_unfiltered
#loading data of co2 emission and population
co2_emissions_tonnes_per_person <- read_csv("Gapminder_data/co2_emissions_tonnes_per_person.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## country = col_character()
## )## See spec(...) for full column specifications.#making the dataframe into long format
country_list <- co2_emissions_tonnes_per_person$country
column_list <- colnames(co2_emissions_tonnes_per_person)
column_list <- column_list[column_list != 'country']
co2_emissions_tonnes_per_person <- co2_emissions_tonnes_per_person %>%
gather(key = "year", value = "co2_emission", column_list)## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(column_list)` instead of `column_list` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.#merging the to datasets
gapminder_co2 <- merge(gapminder2, co2_emissions_tonnes_per_person, by = c('year', 'country'))
#ommitting rows with NA's
gapminder_co2_complete <- drop_na(gapminder_co2, co2_emission)Answering the question with an animated object
#Generating the anim object
anim_co2 <- ggplot(gapminder_co2_complete, aes(gdpPercap, co2_emission, size = pop, color = continent)) +
geom_point() +
scale_size_continuous(label=comma) +
scale_x_log10(labels = scales::comma) + # convert x to log scale
scale_y_log10(labels = scales::comma) + # convert y to log scale
labs(title = "Does polusion rise as people become richer?",
subtitle = "The whole world",
x = 'Gross domestic product (GDP) per capita',
y = 'CO2 emission in tons per capita',
size = 'Population',
color = 'Continent',
caption = 'Data source: www.gapminder.org/data/')+
geom_text(aes(x = min(gdpPercap), y = min(lifeExp), label = as.factor(year)), hjust=-1, vjust = 8, alpha = 0.2, col = "gray", size = 20) +
transition_states(as.factor(year), state_length = 0)
anim_co2## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 2 rows containing missing values (geom_point).#Looking at Europe only
europe <- gapminder_co2_complete %>%
filter(continent =="Europe") %>%
ggplot(aes(gdpPercap, co2_emission, size = pop, color = country)) +
geom_point() +
geom_text(aes(label=country), hjust=0.1, vjust=-0.9, nudge_x = -.04)+ #adjustes the placement of country labels
theme(legend.position = "none") + #removes the caption of countries from the sidebar
scale_size_continuous(label=comma) +
scale_x_log10(labels = scales::comma) + # convert x to log scale
labs(title = "Does polusion rise as people become richer?",
subtitle = "Only Europe",
x = 'Gross domestic product (GDP) per capita',
y = 'CO2 emission in tons per capita',
size = 'Population',
caption = 'Data source: www.gapminder.org/data/') +
geom_text(aes(x = min(gdpPercap), y = min(co2_emission), label = as.factor(year)), hjust=0.1, vjust = -9, alpha = 0.2, col = "gray", size = 20) +
transition_states(as.factor(year), state_length = 0)
europe
Looking at Europe it seems that some countries are worse than others. I want to look at how the worst are comparing to each other by animating a bar chart race inspired by the tutorial mentioned earlier:
##Barchart race
#filtering the 15 most polluting countries in Europe
gapminder_europe_most_co2 <- gapminder_co2 %>%
filter(continent =="Europe") %>%
group_by(year) %>%
arrange(year, desc(co2_emission)) %>%
mutate(ranking = row_number()) %>%
filter(ranking <=15)
head(gapminder_europe_most_co2)## # A tibble: 6 x 8
## # Groups: year [1]
## year country continent lifeExp pop gdpPercap co2_emission ranking
## <int> <fct> <fct> <dbl> <int> <dbl> <dbl> <int>
## 1 1950 Luxembourg Europe 65.7 2.96e5 14555. 25.1 1
## 2 1950 United Kin… Europe 69.0 5.01e7 9767. 9.9 2
## 3 1950 Belgium Europe 66.4 8.64e6 7990. 8.83 3
## 4 1950 Germany Europe 66.5 6.84e7 6090. 7.31 4
## 5 1950 Czech Repu… Europe 64.4 8.93e6 6691. 6.51 5
## 6 1950 Slovak Rep… Europe 60.9 3.46e6 4938. 5.39 6#Creating the bar chart race
anim_race <- gapminder_europe_most_co2 %>%
ggplot() +
geom_col(aes(ranking, co2_emission, fill = country)) +
labs(title = "Bar race of top ranked CO2 emission",
subtitle = "Only Europe",
x = 'CO2 emission in tons per capita',
caption = 'Data source: www.gapminder.org/data/')+
geom_text(aes(ranking, co2_emission, label = 'CO2 emission'), hjust=-0.1) +
geom_text(aes(ranking, y=0 , label = country), hjust=1.1) +
geom_text(aes(x=15, y=max(co2_emission) , label = as.factor(year)), vjust = 0.2, alpha = 0.5, col = "gray", size = 20) +
coord_flip(clip = "off", expand = FALSE) + scale_x_reverse() +
theme_minimal() + theme(
panel.grid = element_blank(),
legend.position = "none",
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
plot.margin = margin(1, 4, 1, 3, "cm")
) +
transition_states(year, state_length = 0, transition_length = 6) +
enter_fade() +
exit_fade() +
ease_aes('quadratic-in-out')
anim_race